@inproceedings{ou-li-2020-ynu-oxz,
    title = "{YNU}{\_}oxz at {S}em{E}val-2020 Task 12: Bidirectional {GRU} with Capsule for Identifying Multilingual Offensive Language",
    author = "Ou, Xiaozhi  and
      Li, Hongling",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.semeval-1.300/",
    doi = "10.18653/v1/2020.semeval-1.300",
    pages = "2251--2257",
    abstract = "This article describes the system submitted to SemEval-2020 Task 12 OffensEval 2: Multilingual Offensive Language Recognition in Social Media. The task is to classify offensive language in social media. The shared task contains five languages (English, Greek, Arabic, Danish, and Turkish) and three subtasks. We only participated in subtask A of English to identify offensive language. To solve this task, we proposed a system based on a Bidirectional Gated Recurrent Unit (Bi-GRU) with a Capsule model. Finally, we used the K-fold approach for ensemble. Our model achieved a Macro-average F1 score of 0.90969 (ranked 27/85) in subtask A."
}